System for optimal physical exercise and training

ABSTRACT

A fitness system for enhancing the effectiveness and efficiency of physical training and/or exercise by a user comprises uses (1) a plurality of sensors that are worn by an exercising user and which generate data concerning monitored body processes pertaining to the exercise&#39;s effects on the user&#39;s body, (2) a processor operates under software control for processing, storing, and analyzing the data, and sending the processed data to a host device using a wireless communication protocol to communicate desired adjustments to the exercise in real time. The host device can be a smartphone, tablet computer or other web accessible device that can display and communicate bilaterally with the processor.

This is a divisional application of U.S. application Ser. No. 14/892,496filed Nov. 19, 2015, now U.S. Pat. No. 9,662,538 issued May 30, 2017,which is a national phase application of PCT/US2014/042330 filed Jun.13, 2014, which claims benefit of Provisional Application No. 61/834,836filed Jun. 13, 2013, each of which are incorporated by reference as ifset forth fully herein.

FIELD OF THE INVENTION

This invention pertains to exercise and fitness systems.

Although there are numerous types of exercise machines and devicesavailable for building strength and/or endurance, there is a need for asystem that assists a user in ensuring that the exercise regimen beingemployed is efficient and effective. For example, there are optimumzones within one should maintain a breathing rate, heartbeat, etc. Therealso comes a point wherein muscles receive insufficient oxygen, so thatfurther exercise or repetitions of an exercise movement are of little orno benefit and, in fact, could cause injury. Moreover, these zones andpoints vary from person to person, and also vary during the course oftraining.

SUMMARY OF THE INVENTION

The fitness system for enhancing the effectiveness and efficiency ofphysical training and/or exercise by a user uses (1) a plurality ofsensors that are worn by an exercising user and which generate dataconcerning monitored body processes pertaining to the exercise's effectson the user's body, (2) a Muscle Exercise Monitoring System (sometimeshereinafter referred to as the “MEMS”) device for processing, storing,and sending data, (3) a host device(s) in digital communication with theprocessor via a wireless communication protocol for receiving theprocessed data and (4) a display device that is preferably a componentof the host device(s) or one or more client interfaces on any webaccessible devices, for display and/or control in a visuallycomprehensible format to the user and/or a trainer.

If desired, means can be provided for establishing a data communicationlink between the processor and a cloud-hosted service that provides fordata aggregation service.

The MEMS herein comprises a processor operating under software controlto record and evaluate the incoming sensor data in order to define,update and communicate desired adjustments to the exercise in real timefor optimal training and exercise. Incoming sensor data, for example,may include pulse rate, breathing rate and capacity, blood and breathchemistry, and the exercise activity rate and number of repetitions incontractions of one or more monitored muscles. Visual or audiblefeedback to the user (or the user's trainer) provided by the host devicemay, for example, advise the user to increase or decrease the rate ofmuscle contraction/release, adjust the rate or volume of breath upwardor downward, etc. Consequently, the system herein provides the trainee,trainer or technician with objective feedback in order to obtain optimalresults from the training/exercise session.

The sensors may, in whole or in part be formed on Spandex® (or othersuitable material) that is placed over a muscle group, skeletal joint,or around the chest cavity, head, wrists or ankles, or feet.

Accordingly, exercise-related data is collected in real time and storedlocally within the processor, a nearby computer, a wearable data storagedevice, and/or other data storage device in direct or indirectelectronic communication with the sensors. A standard wireless datacommunication protocol such as Bluetooth®, IEEE 802.11a/b/g/n, or othersuitable protocol can be conveniently used to display exercise-relateddata and information in real time on the user's smartphone or otherdisplay device. Where a cloud service is utilized, locally stored datacan be uploaded to the cloud when connectivity is established. Cloudsessions can be utilized to permit users to share and/or compareexercise sessions.

Preferably, the processor collecting data from sensors attached to theuser's body is a small wearable device having the dimensions and weightthat permit it to be worn comfortably and barely noticed by the user, ifat all. The processor responds to the incoming data streams from thesensors in accordance with target parameters manually imputed orpre-programmed into the host device, whereby the information and/or datafed back to the user can represent real-time performance parameters, thedifference between actual and optimal parameter values, and/or thedirection or degree to which the exercise movement should be adjusted.The host device may be any web client compatible device such as a smartphone, tablet computer, “wrist-watch” computer or other electronicdevice that can provide a display for viewing by the user. The term“host device” will hereinafter be used to conveniently refer to any orall of the foregoing.

Sensors

The processor provides a number of data ports to which the sensors areelectronically coupled for data communication. Electronic coupling canbe via hard wire, wireless protocol or a combination thereof.

Among the sensors that can be utilized are sensors that measure (1)bioelectrical signals from monitored muscles or muscle group(s), (2)heart rate, (3) blood translucence indicative of the 02 content of theblood, (3) muscle flexure, (4) air flow into and/or out of the user'slungs, (5) pressure under the soles of the feet and (6) physicalposition of the user's body (i.e., data responsive to the position ofvideo references placed on the user' s body for providing photo-metricdata via computer vision). Other sensors may be utilized as well and arewithin the scope of the invention.

The bioelectric sensors preferably comprise electrodes attached to themuscle group in such a way as to collect surface electromyography(“sEMG”) signals that measure the amount of electrical activity releasedby muscles as they contract, due to biochemical ion movement duringmuscle activation and recovery. The preferred muscle sensor does notpenetrate the skin to collect the signal. As a muscle becomes fatiguedthe changes to the bioelectrical signal are measured using spectralanalysis and displayed as frequency power density. The muscle'selectrical signal frequency-power spectrum changes as the monitoredmuscles use adenosine triphosphate (“ADT”) during the exercise andproduce waste products such as CO2 and lactic acid.

In accordance with the invention, these signals are processed by thesystem herein to detect proper technique and muscle fatigue.

Pulse rate oximeters are sensors that measure both pulse rate andoxygen-related blood translucence. Blood oxygen becomes depleted at thepoint of physical exertion, and the detection of depletion produces aprocessed data signal that instructs the user to stop the exercise. Whena muscle is overworked, there is little or no O2 in the muscle, so thereis no benefit from additional reps, and an overworked muscle is alsoprone to injury. Heart rate data is used to enable the user to maintainan exercise level consistent with a target rate that optimizes theburning of fat rather than muscle as an energy source.

Air flow into and out of the user's lungs is derived from changing inthe lung volume as preferably measured by a flexor-type sensor acrossthe chest as to avoid the discomfort and disruption to the user ofpartially blocking the user's mouth or nose. Data from the flexor sensoris used in accordance with the invention to determine breath rate, andto thereby provide feedback to the user regarding breathing rate andvolume and breathe control.

An accelerometer may be used in accordance with the invention to collectkinematic data about body movement and processes to count repetitionsduring repetitive exercise movements and for body-positioninginformation.

An array of pressure sensors attached to a body or placed in staticexercise-mat may be used in accordance with the invention to collectbalance point, pressure under body position, and body position. Allsensors can be read at configurable frequency rates on different phases.The sensors interface with one or more main amplifier units that arepart of the MEMS.

Video data can be generated and processed in accordance with theinvention to provide photometric data.

Processor

The processor, operating under software control, reads and analyses theincoming signals from the sensors. Background noise is calibrated andfiltered. The sensor data is transformed, analysed, compressed,packetized and time-stamped, and saved, and transmitted in a formatcompatible with a chosen wireless communication protocol, preferablyBluetooth®, for display and/or control.

The processor is preferably configured to engage in two-waycommunication with the host device so that target parameters can becommunicated manually or under software control to the processor by thehost device. Configuration of the processing unit is pushed by any PC atthe initial setup via USB or ad-hoc WiFi connectivity. Future updatesand configuration of the unit can be done through admin module of theweb client interface. The target parameters are used by the processor inconjunction with real-time sensor data during the exercise/trainingsession to transmit data for audible or visual presentation to the userpertaining to desired adjustments to the exercise routine for optimalresults. Software and algorithms within the processor can be utilized toconfirm that the muscle sensor is properly placed, since the signalpattern from the monitored muscle is predictable.

Host

The host device is preferably a smartphone, tablet computer or“wrist-watch” computer, but can also be any other electronic device thatcan provide a display that is viewable by the user, and preferably hostprocessing software. Ideally, the smaller devices are preferred becausethe user can easily carry it on their body when running or movingaround.

The host device preferably receives data from the processor via wirelesscommunication, and preferably via a commonly available communicationsprotocol such as Bluetooth®, displaying the data in a form that providesvisual or audio feedback to the user as to how the then-current exercisemovement pertains to their body's ideal workout and/or what changes theuser should make to the movement at the moment. Signal data is processedunder software control within the processor device, utilizing a waveletanalysis algorithm to transform from time domain to frequency domain,remove redundancy, compress and scale signals. The muscle group or otherbody process to be monitored can be menu-selected on the host device sothat the incoming sensor signals may be filtered and processed foroptimum signal-to-noise ratios and optimal display speeds.

The algorithm utilizes data from each repetition of the monitoredexercise movement (i.e., the contraction and release of the monitoredmuscle or muscle group) including the duration and intensity of thecontractions and releases. The collected reps for the exercise areconveniently hereinafter referred to as a “session”. The data pertainingto each repetition of the session is preferably stored in a separaterecord, with the collection of records representing the session's data.

The duration and intensity of each monitored rep is compared by theprocessor to a desired duration and intensity, and transmitter to thehost device to provide visual and/or audible feedback to the user isgenerated to speed up or slow down the movement or adjust the resistanceto the movement to obtain optimum results.

The hosted application is preferably capable of searching for, andcommunicating with, one or more nearby fitness computers (processordevice) so that the host can send commands or other data to selectedcomputers and receive streamed data signals from each such fitnesscomputer in a manner that relates incoming data to the computer thatgenerated it. The processor device processes the signal data to create areal-time model of the user's exercise variables that enable the user totailor the exercise for optimum results by comparing exercise signaturesignals. Accordingly, visual and/or audible cues can be generated backto the user in real time as data is acquired, processed and analysed.

One host device can monitor multiple processors, or one trainee can bemonitored by multiple host devices, depending on how the system isconfigured. The processed data can indicate when the monitored muscle isfatigued to the point where the exercise flexure/extension should stop,whether the breath rate and pulse rate signatures are correct, whetherthe muscle is being used correctly, whether the heart rate is proper forthe breath rate, the number of reps experienced, whether the user isinhaling and using O2 properly, whether the muscle is using O2efficiently, etc. All of this can be combined to determine if the useris exercising correctly and optimally. The display can show the userthat the proper muscle(s) arc being worked, when the monitored musclegroup is properly exhausted, and whether the exercise movement should besped up or slowed down to match the ideal waveform for that movement.

These and further details of the invention will be apparent to those ofordinary skill in the art from reading a description of the preferredembodiment of the invention described below, and of which the drawingforms a part.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a preferred system, constructedin accordance with invention;

FIG. 2 is a block diagram of a preferred host device used in accordancewith the invention;

FIGS. 3A-3C are partial figures of a circuit diagram of the preferredprocessor 50 illustrated in block diagram form in FIG. 1, while FIG. 3Dshows the whole formed by the partial figures and indicates thepositions of the parts;

FIG. 4 illustrates a user wearing the preferred breath rate sensor;

FIG. 5 illustrates an alternative embodiment of the invention;

FIG. 6 illustrates an alternative embodiment of the host device;

FIG. 7 is a block diagram illustrating the preferred MEMS systemworkflow;

FIG. 8 is a block diagram illustrating the preferred MEMS firmware;

FIG. 9 is a block diagram illustrating Web interface workflow;

FIG. 10 is an illustration of a preferred Web interface landing page;and

FIG. 11 is an illustration of representative data displayed on a hostdevice or display device associated with the system herein.

CURRENTLY PREFERRED EMBODIMENT

Preferred MEMS System Components

FIG. 1 is a block diagram illustrating a preferred system constructed inaccordance with invention wherein a muscle sensor 10, a pulse sensor 20and a breath rate sensor 30 are electrically coupled for communicationwith an analog-to-digital converter 40.

A muscle signal amplifier associated with the muscle sensor 10preferably has a very high input impedance of approximately 10terra-ohms, and receives an electrical signal from the monitored musclethrough the user's skin. The preferred muscle sensor comprises threeelectrodes, one of which is positioned over a suitable location such asthe user's elbow, to act as a signal reference point. The remaining twoelectrodes are preferably places at the end of the muscle and the middleof the muscle, respectively. Alternatively, it may be desirable to placethe two remaining electrodes on alternate sides of a monitored muscle.

The sensors may include electrodes that are formed on Spandex® (or othersuitable material) that is placed over a muscle group, skeletal joint,or around the chest cavity, head, wrists or ankles, or feet.

The preferred pulse sensor 20 is a Pulse Sensor Amped™ offered atwww.pulsesensor.com. It includes a photochromatic blood oxygen sensorthat measures the color of the user's blood through the skin, and mayconveniently be attached at the ear lobe or fingertip.

The preferred breath rate sensor (also illustrated in FIG. 4) is of thechest strap variety and measures chest deflection and deflection rate;the chest strap is preferred because it is more comfortable and lessdistracting than other type of sensors such as those in mask for thatfit over the nose or mouth.

Other sensors may be utilized as well, but the foregoing are illustratedand described for illustrative purposes, and it should be understoodthat the invention is not limited to utilization of the foregoingsensors.

The sensors generate signals in response to the monitored bodyparameter. The muscle sensors, for example, generate a train of pulsesas ions move as the result of biochemical reactions during musclecontractions and releases during repetitive exercise movement. Theremaining sensors produce analog signals as well, which may be generallysinusoidal (as the breath rate sensor), spiked (as the pulse ratemonitor) or relatively ramped (as the O2 monitor).

The signal from each sensor is digitized by the analog-to-digitalconverter 40, and the resulting signal values are fed over a bus to theCPU of a processor 50 which processes, compresses, packetizes, and timestamps the sensor signal values, and then transmits the packetswirelessly to a host device via a Bluetooth® communicator so that theuser's host device, and/or that of a desired third party, can beutilized to store and/or display the sensor data in a format easilyassimilated by the user, either visually and/or audibly as hereinafterdescribed. The currently preferred processing algorithm is from anopen-source library called the WAILI wavelet transform library.

Each of the preferred sensors can include all the components illustratedin FIG. 1, including its own A/D converter and Bluetooth® transmitter;alternatively, the sensors can be multiplexed to time-share the A/Dconverter and Bluetooth® transmitter. Other wireless communicationstandards can be used that are compatible with the chosen host device(s)without departing from the scope of the invention.

FIG. 2 is a block diagram of a preferred host device used in accordancewith the invention, and is shown to include means for de-packetizing theincoming wirelessly transmitted data, decompressing the resulting dataif necessary, storing it at least temporarily and displaying it in areadily understandable format to a viewer that is most likely the user.Compressing and packetizing the data permits a maximum amount of data tobe transmitted within the restricted amount of wireless transmissionbandwidth available. Data is preferably recovered using statisticalwavelet analysis; decompression may accordingly not be necessary if thewirelessly transmitted data represents, at least in part, sensor signalsthat have been converted from time-domain values to frequency-domainvalues as part of the signal processing done within the processor deviceof FIG. 1 to thereby inherently reduce the amount of data needed to betransmitted to a level meeting any limitations of available bandwidth.The preferred host device can communicate bilaterally with the processorto enable the user to input desired exercise or body functionparameters, set targets, etc.

FIGS. 3A-3C illustrate a circuit diagram of the preferred processor 50illustrated in block diagram form in FIG. 1. The processor includes apair of input jacks JP3, JP10 to which a pair of muscle-monitoringsensors can be respectively electrically coupled. Two additional, jacksJP1, JP13 are provided for electrically coupling the pulse sensor andbreathe rate sensor to the processor.

There are three electrodes associated with each of the preferred musclesensors, and each of the jacks JP3, JP10 accordingly have threeconnection points. Two of the electrodes of a muscle sensor arepreferably positioned to monitor the left and right sides of themonitored muscle or muscle group or, alternatively, complementarymuscles such as the user's bicep and tricep, with the two consequentialsignals being respectively coupled to pins 2 and 3 of the jack. Thethird electrode is preferably positioned at a signal reference point onthe user's body and coupled via pin 1 to a circuit “ground” referencepoint; the user's elbow is a good reference location since there isvirtually no muscle activity at that point that adversely affects signaldetection and processing.

The two signals from each sensor are respectively coupled by the jacksto input pins 1 and 4 of a differential amplifier, preferably an AnalogDevices AD8220. Thus, jack JP10 couples the applied sensor signals atpins 2 and 3 to the input pins 4 and 1 of amplifier U9 as illustrated,and JP3 does so to amplifier U3. An active low-pass filter comprisingamplifier U11 (preferably an Analog Devices OP117) is electricallycoupled between the output of amplifier U9 and the “reference” pin 6 ofthat amplifier as illustrated. U11 is a differential amp and minimizesenvironmental EMI.

Similarly, an active low-pass filter comprising amplifier U13 iselectrically coupled between the output of amplifier U3 and its“reference” pin 6 as illustrated. Active filters are preferred becausethey provide a sharp cut-off frequency. Active filter amplifier U13provides negative feedback in effect to reduce the gain of amplifier U3and thereby avoid amplification of high frequency noise. U8 asconfigured, functions as a DC block and an RMS block and increases theinput impedance. DC block rejects EMI that can cause DC drift so to getpure AC at output to the processor device.

The analog signals indicative of the real-time muscle sensor signals atjack JP10 and JP3 are a train of pulses in nature, and are respectivelyoutputted from pin 7 of amplifiers U9 and U3 to the non-inverting inputpin 6 of respective RMS-to-DC converters US5 and U8 (preferably AnalogDevices AD8436 units). DC level signals representing the RMS values ofthe respective sensor signals are outputted from pin 15 of therespective converters, and respectively applied as first and secondinputs to pins 10 and 7 of a four channel analog-to-digital converter U4(preferably an Analog Devices AD7994 configured as illustrated).

The remaining two input channels of the analog-to-digital converter U4are electrically coupled to pin 2 of input jacks JP1 and JP13,respectively, to receive signals from the pulse sensor and breathesensor. The signals from these sensors are modulated DC in nature.

In accordance with the preferred embodiment of the invention, all sensorsignals are thereby sampled in a multiplexed manner by converter U4 andconverted to digital data that is outputted in a multiplexed manner atpin 14 of converter U4 for subsequent transmission to the host devicefor display. Synchronous circuits are used to and sample the sensorsignals at precise moments in time on a cycle basis, channels 1 & 2 areon 10 khz, channels 3&4 are on 500 Hz. These analog signals arerepresented as a series of digital bits and the number of bits in thesystem defines the resolution of the conversion. With applyingtraditional sampling theory (i.e. Nyquist-Shannon theorem), a bandlimited sensors signal are represented with a quantifiable error bysampling the analog signal at a sampling rate at or above that samplingrate.

Prior to processing the digital values for wireless transmission, thedigital data from the converter is read by the processor over I2Cinterface. The signal data is transformed from the time-amplitude domaininto the time-frequency domain using a wavelet transformation. Thistransformation enables quick filtering and analysis of how the frequencyresponse varies in time, decomposition of the spectral components andthe comparison of spectral signatures.

With bidirectional communication possible, targets and algorithms can beupdated from via Internet or cellular communications as, for example, bystoring same on Flash ROM within the processor or host device.

FIGS. 5 and 6 illustrate an alternative embodiment of the invention.FIGS. 5 and 6 are analogous to FIGS. 1 and 2. In this embodiment, signalprocessing is done in the host device. Accordingly, sensor signals aredigitized, compressed and packetized prior to wireless transmission. Noconversion from time domain to frequency domain values is performedprior to wireless transmission, although one could do so if one wished.The digitized values are de-packetized, decompressed and analyzed withinthe host device, which then displays data for viewing by the user inlight of parameter targets programmed into the host device.

Preferred MEMS Software Components

FIG. 7 illustrates overall software workflow. The operating system onthe MEMS is a customized Raspbian distribution. The kernel remainslargely unmodified but certain libraries such as i2c-tools have beeninstalled and configured to allow access to and communication over thei²c bus. Firmware enables WiFi, Bluetooth, USB and Ethernet connectivityon the boot. The firmware communication modules are written in C code.The C code utilizes pthread and i²c libraries to read from the sensorsand present the data to a client connected over a top socket.

FIG. 8 is a block diagram of the data collection and socket servermodules. I2C registry is configured to read multiple sensor data at 25Microhertz. On each reading cycle, data is being parsed and identifiedtype of sensor. Reading voltage value is calibrated and filtered tonormalized value. There is a Python client script that runs as part ofthe web interface code that collects data from the C sensor poller andpasses the data to the clients and user interfaces.

When the RaspberryPi device loads, all communication modules areconfigured on the boot. Raspbian_Wifi_Setup.sh loads the wifi.conf filefrom attached usb drive and configures the WiFi. Once the device boots,it loads a script to connect the device to a wireless network whosecredentials are loaded at boot from a flash drive. The credentials arestored on the flash drive in the correct format by entering the correctinformation into a Java program running on the configuring user'scomputer. Then trunc.sh runs and clears the local disk storage freeingup space for a new session. Then pollreg.bin and asynchronously collectssensor data and serves that up through a tcp socket to browser clientfor display and analysis.

Sensor data will begin collection and broadcast in real time as soon asthe device is powered on using the data communication and tcp socketmodule.

On every 25 microsecond, i2c bus read by data collection routine onmultithread application. After parsing bus data, each channel has itsown thread and handles memory array by using continues wavelettransforms theory. As a result, sensor data is converted intotime-domain. Basically, each digital reading value has date/time stampson a millisecond level. Graph engine modules read from the memory arrayand then draw the dots for each value/time and connect each point oncontinuous bases. For predicting the behaviour of muscle, heart andbreath activities, time-series data is analyzed and classified based onBayesian regression methods. Classified data is historically saved andrun through machine learning engines where linear regression andspectral clustering are being used to generate next set of data for anygiven time-frame.

FIG. 9 is a workflow of the web application interface of the MEMS andhow sensor data is being displayed on interface. On the MEMS, a Capplication polls the sensors every twenty-five microseconds. It storesthe values from each sensor with a date and timestamp (i.e. [[channel1,channel2, channel3, channel4, datetime], . . . ]). The C applicationalso opens a socket for communication with a web service. On thegraphing page of the web application, an AJAX script is run every 250microseconds. This script calls a python script that connects to thesocket on the MED. It pulls all data stored in memory, and passes itback to the web application. The web application then parses the dataand puts it the appropriate graph.

Signal data is read and processed as time series data. Fractal behaviourmethods are used on display sensor data. While monitoring muscleactivities, the host can detect unique behaviour of target (musclegroups/exercise type etc). Wavelets methods and algorithm are used todetect specific behaviours.

All sensor data, Muscle, Breath, and Heart Rate sensors combineddisplay/chart/archive overall Aerobic-endurance, Anaerobicpower-endurance, sustained-power, and strength-power activities. Thecombined sensor data perceives ATP and CP depletion, lactic acidaccumulation/fatigue effect, and calcium ion build up in the muscles atany given time, as well as, VO2 max data through oxygenconsumption/depletion and blood pH decrease. This and basic datainformation, such as height, weight, age, sex, activity level, bmi, etc.produce an accurate analysis of the users overall ability, strength, andendurance at any given exercise. The user's calorie, fat burned, energyconsumption/fuel, and recover times can be calculated. The datacollected can be recorded and collected in physical activities naturalenvironment via in practice, in game, the gym etc.

Using collected flex sensor data, MEMS determines the oxygen inhaled andexhaled by heart rate readings equal O₂ consumption and presents it tothe user via web interface. Results for VO2 measurements are generallydisplayed in L/min (i.e. litres per minute, representing the volume ofoxygen consumed by the user's entire body each minute) or, to accountfor differences in total body mass, in mL/kg/min (i e milliliters perkilogram per minute, representing the volume of oxygen consumed eachminute per kilogram of body mass).

The method for measurement of VO2 can be summarized according to thefollowing equation . . .VO2=[VI×% O2VI]−[VE×% O2VE]

-   -   where VI=volume of inspired air;        -   % O2VI =percent oxygen in inspired air (flexor sensor);        -   VE=volume of expired air (flexor sensor); and        -   % O2VE=percent oxygen in expired air

Using Flexor and heart rate sensors together, MEMS monitors O₂consumption and maximum exertion. Within the above equation, VO2 maxequals maximum millilitres of oxygen consumed in a minute/body weight(in kilograms).

Using Flexor, muscle and heart rate sensors together, it is possible todetermine ATP & CP depilation. O₂ intake and heart rate during muscularcontraction from peak performance to fail equals ATP & CP depletion.Muscle recovery rate is calculated from monitored heart rate recoveryand monitored breath consumption following cessation of exercise. MEMSalso enable to monitor the strength of muscle on calculation peakperformance. The following methods are used to calculate peakperformance for different user profile.

For men: Calories Burned=[(Age×0.2017)+(Weight×0.09036)+(HeartRate×0.6309)—55.0969]×Time/4.184.

For Women: Calories Burned=[(Age×0.074)—(Weight×0.05741)+(HeartRate×0.4472)—20.4022]×Time/4.184.

Data Description

The application will be divided into a few primary data objects:Session, Person, and Company.

Data Dictionary

Session Group

-   -   Session ID    -   Device ID    -   Company ID    -   Patient ID    -   Health Professional ID    -   Session Start Date/Time    -   MEMS identifier Sensor Data        -   Time:Sensor Value

Session ID: A unique ID for each test session.

Device ID: A unique ID for MED used for particular session.

Company ID: Reference to the company operating the device.

Patient ID: Reference to the patient being tested.

Health Professional ID: Reference to the individual performing the test.

Session Start Date/Time: Timestamp of the start of the session.

MED Data: An array of key/value pairs for time to sensor value. (I.e.Data[[channel1, channel2, channel3, channel4, datetime], . . . ])

Person Group

-   -   Person ID    -   Company ID    -   Person Type ID    -   Name ID    -   Address ID    -   Mobile Phone    -   Home Phone    -   Work Phone    -   Email

Person ID: A unique ID for the person.

Company ID: Reference to the person's company (nullable).

Person Type ID: Reference to the type of person.

Name ID: Reference to the person's name.

Address ID: Reference to the person's address.

Mobile Phone: The mobile phone number.

Home Phone: The home phone number.

Work Phone: The work phone number.

Email: The person's email.

Company Group

-   -   Company ID    -   Company Name    -   Address ID    -   Office Phone    -   Office Email    -   Web Address

Company ID: A unique ID.

Company Name: The company's name.

Address ID: Reference to the company's address.

Office Phone: The mobile phone number.

Office Email: The person's email.

Web Address: The URL of the company's site.

User/Client Name

-   -   First Name    -   Last Name

Address

-   -   Line 1    -   Line 2    -   City    -   State    -   Zip

Person Type

-   -   Patient    -   Health Professional

Human Interface Design

FIGS. 10 and 11 illustrate a web client interface mock-up. The landingpage (FIG. 10) is a sample page where users are able to enter the basicinformation for a user. The form accepts the following data:

-   -   First name    -   Last name    -   Address    -   Home Phone    -   Mobile Phone    -   Work Phone    -   Email

Once all data is entered, the user clicks a “Begin Monitoring” button totell the website to start collecting and plotting data on the graphs inthe “Display Detail Page” (FIG. 11).

In one currently conceived configuration, for example, the user wearsthe sensors while exercising, and can view the sensed data in a graphicand/or tabular form on a smartphone or tablet computer that is inwireless communication with the processor to get real time feedback. Thedisplay can further provide visual and/or audio clues as to how the usercan adjust the exercise movement for more efficient gain, such as bybreathing more deeply, and more (or less) often, accelerating or slowingthe pace of the movement, etc.

Although the present invention and its advantages have been described indetail, it should be understood that various changes, substitutions andalterations can be made herein without departing from the spirit andscope of the invention as will be defined by appended claims.

The invention claimed is:
 1. A fitness system, comprising: a plurality of sensors which collect data related to at least one of a user's pulse rate, breathing rate, breathing capacity, blood chemistry, breath chemistry, exercise activity rate, and number of contractions of one or more monitored muscles of said user; a wearable device comprising a processor operating under software control, said processor comprising at least one differential amplifier, at least one converter, and a multiplexer, said processor in communication with said sensors and configured for receiving said data in real time from said plurality of sensors, processing said data into processed data, and sending said processed data to a host device, said processing said data by said processor comprising: receiving by the at least one differential amplifier at least some of said data and generating respective sensor signals and an RMS value associated with each of said sensor signals; converting by said at least one converter said RMS values to DC values; sampling by said multiplexer said DC values to produce a multiplexed digital signal representing the sampled values; and generating feedback information according to manually inputted or pre-programmed parameters in the host device and based on said multiplexed digital signals, wherein said feedback information represents real-time performance parameters, the difference between actual and optimal parameter values, and/or the direction or degree to which the exercise movement should be adjusted; a host device in communication with said processor and configured for receiving said feedback information; and a display device in communication with said host device and configured for displaying said feedback information.
 2. The fitness system of claim 1, wherein said display device is integrated with said host device.
 3. The fitness system of claim 1, wherein said processor is coupled via a data communication link to a cloud-hosted service for providing a data aggregation service.
 4. The fitness system of claim 1, wherein said processor is configured to communicate with an external computer.
 5. The fitness system of claim 1, wherein said processor is configured to communicate with a wearable data storage device via a wireless communication protocol.
 6. The fitness system of claim 1, wherein said display device comprises a smartphone.
 7. The fitness system of claim 1, wherein said host device comprises a web client compatible device.
 8. The fitness system of claim 1, wherein said host device is selected from the group consisting of a smart phone, a tablet computer, and a wrist-watch computer.
 9. The fitness system of claim 1, wherein said processor further comprises: local memory for locally storing data from said sensors; means for electronically communicating with a data-receiving cloud service; and means for uploading said locally stored data to said cloud service.
 10. The fitness system of claim 1, said plurality of sensors comprising: at least one muscle sensor; a breath-rate sensor; and a pulse-rate sensor. 